bush10(rrcovNA)
bush10()所属R语言包：rrcovNA
Campbell Bushfire Data with added missing data items (10 percent)
坎贝尔森林大火的数据添加丢失的数据项（10％）
译者：生物统计家园网 机器人LoveR
描述----------Description----------
This data set is based on the bushfire data set which was used by Campbell (1984) to locate bushfire scars - see bushfire in package robustbase. The original dataset contains satelite measurements on five frequency bands, corresponding to each of 38 pixels. The data set is very well studied (Maronna and Yohai, 1995; Maronna and Zamar, 2002). There are 12 clear outliers: 33-38, 32, 7-11 and 12 and 13 are suspect.
坎贝尔（1984年）这是被定位森林大火疤痕的森林大火的数据集，该数据集的基础上 - bushfire包robustbase。的原来的数据集包含SATELITE的的测量五个频带，对应于每38像素。该数据集是很好的研究Maronna和Yohai，1995年Maronna Zamar，（2002年）。有12个明确的异常值：33~38，32，7-11，12和13是可疑的。
用法----------Usage----------
data(bush10)
格式----------Format----------
A data frame with 38 observations on 6 variables.
38观察6个变量的数据框。
The original data set consists of 38 observations in 5 variables. Based on it four new data sets are created in which some of the data items are replaced by missing values with a simple "missing completely at random " mechanism. For this purpose independent Bernoulli trials are realized for each data item with a probability of success 0.1 where success means that the corresponding item is set to missing.)
在5个变量的原始数据集包含38个观察。基于其上的四个新的数据集的数据项创建在其中一些所取代缺失值，用一个简单的“完全随机缺失”机制。为了这个目的实现独立的贝努利试验的每个数据项的成功0.1意味着对应的项目被设置为缺少成功的概率。）
源----------Source----------
Maronna, R.A. and Yohai, V.J. (1995) The Behavoiur of the Stahel-Donoho Robust Multivariate Estimator. Journal of the American Statistical Association 90, 330–341.
Maronna，R.A. Yohai，V.J. （1995）Behavoiur的Stahel Donoho提出的鲁棒多变量估计。 [美国统计协会90，330-341。
Beguin, C. and Hulliger, B. (2004) Multivariate outlier detection in incomplete survey data: the epidemic algorithm and transformed rank correlations. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 127, 2, 275–294.
Beguin，C.和Hulliger的，B.（2004）多元离群点检测不完整的调查数据显示：疫情算法和转化的等级相关。杂志的英国皇家统计学会系列B（统计方法）127，2，275-294。
实例----------Examples----------
## The following code will result in exactly the same output[＃下面的代码将导致在完全相同的输出]
## as the one obtained from the original data set[＃作为一个从原始数据集获得的]
data(bush10)
plot(bush10)
CovNAMcd(bush10)
## Not run: [＃不运行：]
## This is the code with which the missing data were created:[＃这是与丢失的数据创建的代码：]
## Creates a data set with missing values (for testing purposes)[＃创建一个数据集缺失值（用于测试目的）]
## from a complete data set 'x'. The probability of[＃从一个完整的数据集的“X”。的概率]
## each item being missing is 'pr'.[＃每个项目所缺少的是“公关”。]
##[＃]
getmiss <- function(x, pr=0.1){
library(Rlab)
n <- nrow(x)
p <- ncol(x)
bt <- rbern(n*p, pr)
btmat <- matrix(bt, nrow=n)
btmiss <- ifelse(btmat==1, NA, 0)
x+btmiss
}
## End(Not run)[＃（不执行）]
转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。
注：
注1：为了方便大家学习，本文档为生物统计家园网机器人LoveR翻译而成，仅供个人R语言学习参考使用，生物统计家园保留版权。
注2：由于是机器人自动翻译，难免有不准确之处，使用时仔细对照中、英文内容进行反复理解，可以帮助R语言的学习。
注3：如遇到不准确之处，请在本贴的后面进行回帖，我们会逐渐进行修订。
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发表于 2012-9-28 20:38:03
